The rapid proliferation of 6G-enabled consumer electronics (CE) has introduced significant security and privacy challenges. Traditional security mechanisms often fall short in addressing these issues due to the unique characteristics of CE networks. To enhance intrusion detection systems (IDSs), data-driven artificial intelligence (AI) approaches have gained considerable attention. Nonetheless, AI-based IDSs face challenges related to scalability, privacy preservation, and high computational demands—particularly when handling high-dimensional and complex data. To overcome these limitations, this article proposes a novel framework called Blockchain and Quantum Federated Learning (BQFL). BQFL integrates blockchain technology and quantum computing (QC) with FL to provide an efficient, secure, robust, and privacy-preserving solution for intrusion detection in CE environments. Specifically, blockchain enables fault-tolerant and decentralized trust management for parameter aggregation on the quantum FL (QFL) server. Furthermore, the framework leverages the high-speed and low-latency capabilities of 6G networks to enable real-time and secure data processing and communication among a vast number of CE devices. We validate the effectiveness of the proposed framework through extensive experiments on the ToN-IoT dataset.
The Internet of Things look out on growing security and privacy defies, principally in light of the up growth of quantum threats. To handle these defies, we suggest a unified security framework that merges post-quantum blockchain technologies and zero-knowledge proofs (ZKPs) to attain secure authentication, decentralized identity management, and advanced data protection. The provided system based on a power-weighted consensus mechanism, compressed and overlapping recursive ZKPs, and transaction batching to decrease on-chain load. The outcomes display that the suggested system outperforms conventional systems and state-of-the-art solutions, with response time reduced to 92 ms, transaction throughput increased to 735 tx/s, energy consumption reduced to 0.37 J/op, and authentication accuracy increased to 97.6%, achieving a privacy score of 0.91.These outcomes emphasize that the offered framework not only attains superior performance but as well supplies strong resistance to quantum attacks and high privacy warranties, making it a promising solution for securing future IoT environments.
Hayder A. Nahi, Rusul A. Salman, Awring Falah Hassan et al.· Discover Computing· 0 citations
This paper introduces Data Communities as a novel paradigm for privacy-preserving, blockchain-enabled cooperative digital infrastructures, formalized within the Cooperative Digital Infrastructure (CDI) framework and formalizes privacy guarantees through an adversarial model encompassing classical, quantum, insider, and governance-level threats.
This work presents a lightweight, blockchain-secured distributed IDS for IoT networks that combines anomaly-based detection using federated learning, Snort-based signature detection, and host-based log analysis with transformer models and mitigates the impact of malicious updates.
Charles Stolz, Jielun Zhang· PeerJ Computer Science· 0 citations
As distributed cloud infrastructures grow, the demand for secure, decentralized, and resilient communication solutions with the ability to safeguard sensitive data against growing quantum computing attacks and advanced cyber threats has increased. In response to these challenges, this research introduces a Quantum-Inspired Blockchain-Assisted Harris Hawk Optimization (QB-HHO) algorithm that establishes a hybrid decentralized security architecture that combines quantum-resistant cryptographic techniques with blockchain-based trust management. The proposed framework is implemented and evaluated in the CloudSim Plus simulator using the Google Cluster Workload Trace dataset, enabling realistic assessment of large-scale cloud environments. QBHHO optimally selects secure communication paths, dynamically manages trust scores, and increases the efficiency of the blockchain consensus while ensuring data confidentiality and integrity. A comparison of proposed approach with Multi-Agent Deep Learning (MADL), Enhanced Convolutional Temporal Network (EnCTN), and Blockchain-enabled Security Architecture for Fog Networking Environment (B-SAFE) is performed to verify the performance of the proposed approach. Experimental results show that QB-HHO provides better security and network performance with ${9 9. 2 1 \%}$ Data Integrity, 98.84% Confidentiality, 97.63% Packet Delivery Ratio (PDR), 1.82 s Link Failure Recovery Time and 96.75% Network Lifetime. In addition, QB-HHO enhances Data Integrity (8.94%), Confidentiality (10.37%), PDR (9.12%) and Network Lifetime (12.46%) over the existing methods and reduces Link Failure Recovery Time by 36.58% over the existing methods. The findings demonstrate that the proposed QB-HHO framework offers a comprehensive, scalable, and sustainable security paradigm for decentralized cloud systems, guaranteeing secure communication, tamper resistance, and resilience to traditional and quantum threats.
G. Sujatha, Sajal Mandal· 2026 Third International Con...· 0 citations
Secure and transparent system for recording and verifying digital transactions across distributed networks. Distributed blockchain consensus is achieved through decentralized protocol rules, cryptographic authentication mechanisms, and scalable energy-efficient operations. The present study applies Quantum Mayfly Optimization (QMFO) within a blockchain-based collaborative intrusion detection framework. Collaborative intrusion detection systems (CIDS) have certainly carved their valued place in enhancing modern cybersecurity in the complex landscape of cyber threats. What the BCIDF brings into the picture is a new radical avenue to enhance the detection of new threats and information sharing. In this respect, the proposal cohesively combines distributed blockchain technology and collaborative intrusion detection to increase security, transparency, and trust within cyber realms. Fine-tuning the model parameters will improve blockchain classification accuracy and efficiency, and O(QMFO), a bio-inspired hybrid algorithm inspired by the principles of quantum leaf-edge swarm behavior, is directed toward ensuring the security and performance of blockchain networks. Quantum Mayfly optimization (QMFO) and a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF) are designed to secure distributed networks by allowing tamper-resistant sharing of alerts in the case of an attack across the blockchain. The term 'Quantum Mayfly Optimizer (QMFO)' here is used to amplify performance, speed, and accuracy. Integration, therefore, guarantees the best detection and few false positives, and ensures adaptive actions against upcoming threats.
M. Savitha, I. P. Stella Mary, A.Manikandan et al.· 2026 6th International Confe...· 0 citations
The rapid development of the Internet of Things (IoT) has placed considerable pressure on both security and stability in heterogeneous, resource-constrained networks. In such dynamic environments, trust management is a central issue to determine which service providers can be trusted and to combat malicious activity. Although blockchain-based solutions have offered a means for decentralized, tamper-resistant trust management, most rely on classical cryptographic primitives, which are vulnerable to future quantum computing attacks. This study proposes a Quantum-Resistant Blockchain-Based Trust Management (QR-BCTM) framework in which Post-Quantum Cryptographic mechanisms, Permissioned Blockchain Platform, and Fog-assisted Trust Management architecture are combined and utilized in IoT networks. The framework introduces a quantum-aware trust computation model that combines behavioral trust, indirect recommendations, and a cryptographic assurance score quantifying each participant’s compliance with security requirements. Trust evidence is compressed to reduce blockchain storage and communication overhead, while the hierarchical fog-blockchain architecture offloads computationally intensive operations from resource-constrained IoT devices. The performance of the framework has been simulated in the presence of an adversary, including bad-mouthing, ballot-stuffing, on-off behavior, and identity attacks using a Sybil-type mechanism. Trust accuracy, false trust acceptance, communication overhead, and computation cost were measured, and a sensitivity analysis on the trust-weight parameters was performed. The simulation results suggest that QR-BCTM can enhance the accuracy of trust evaluation, mitigate the impact of malicious nodes, and remain scalable and efficient despite the existing cryptographic overhead. Post-quantum digital signatures and formal security analysis provide protection against quantum-era threats and attacks, while classical threats are mitigated through behavioral trust aggregation and recommendation filtering. In summary, QR-BCTM provides a scalable, simulation-validated and quantum-aware framework for trustworthy IoT network operation, offering practical guidelines for future deployment and prototyping.
M. A. Al-Khasawneh, D. Alsekait, K. Alkayid et al.· Scientific Reports· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduAug 18, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
